import cv2
import numpy as np

def preprocess(gray):
    # # 直方图均衡化
    # equ = cv2.equalizeHist(gray)
    # 高斯平滑
    gaussian = cv2.GaussianBlur(gray, (3, 3), 0, 0, cv2.BORDER_DEFAULT)
    # 中值滤波
    median = cv2.medianBlur(gaussian, 5)
    #cv2.imshow('gaussian&media', median)
    #cv2.waitKey(0)
    # Sobel算子，X方向求梯度
    sobel = cv2.Sobel(median, cv2.CV_8U, 1, 0, ksize=3)
    # 二值化
    ret, binary = cv2.threshold(sobel, 170, 255, cv2.THRESH_BINARY)
    #print("阈值:",ret)
    #cv2.imshow('binary', binary)
    #cv2.waitKey(0)
    # 膨胀和腐蚀操作的核函数
    element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 1))
    element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 7))
    # 膨胀一次，让轮廓突出
    dilation = cv2.dilate(binary, element2, iterations=1)
    # 腐蚀一次，去掉细节
    erosion = cv2.erode(dilation, element1, iterations=1)
    #cv2.imshow('erosion', erosion)
    #cv2.waitKey(0)
    # 再次膨胀，让轮廓明显一些
    dilation2 = cv2.dilate(erosion, element2, iterations=3)
    #cv2.imshow('dilation2', dilation2)
    #cv2.waitKey(0)
    return dilation2

def findPlateNumberRegion(img,ImageArea):
    region = []
    # 查找轮廓,contours记录了每一个闭合的轮廓索引
    image_process,contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    #print("轮廓数：",len(contours))
    #print("原图面积",ImageArea)
    edge=ImageArea*0.016
    #print("阈值：",edge)
    # 筛选面积小的
    for i in range(len(contours)):
        cnt = contours[i]
        # 计算该轮廓的面积
        area = cv2.contourArea(cnt)
        #print("面积：",area)
        # 面积小的都筛选掉
        if (area < edge):
            continue
        # 轮廓近似，作用很小
        epsilon = 0.001 * cv2.arcLength(cnt, True)
        approx = cv2.approxPolyDP(cnt, epsilon, True)
        # 找到最小的矩形包围轮廓，该矩形可能有方向
        #返回矩形的中心点坐标，长宽，旋转角度[-90,0)
        rect = cv2.minAreaRect(cnt)
        #print("rect is: ",rect)
        # box是四个点的坐标
        box = cv2.boxPoints(rect)
        #取整
        box = np.int0(box)
        # 计算高和长
        height = abs(box[0][1] - box[2][1])
        width = abs(box[0][0] - box[2][0])
        # 车牌正常情况下长高比在2.7-5之间
        ratio = float(width) / float(height)
        #print("ratio: ",ratio)
        if (ratio > 5 or ratio < 2):
            continue
        region.append(box)
    print("车牌区域：",region)
    print("车牌个数：",len(region))
    return region

def detect(img):
    """截取车牌"""
    # 转化成灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    #输入图像面积
    height, width = img.shape[:2]
    ImageArea=height*width
    # 形态学变换的预处理
    dilation = preprocess(gray)
    # 查找车牌区域
    region = findPlateNumberRegion(dilation,ImageArea)
    # 用绿线画出这些找到的轮廓
    for boxf in region:
        cv2.drawContours(img, [boxf], 0, (0, 0, 0), 2)
    ys = [boxf[0, 1], boxf[1, 1], boxf[2, 1], boxf[3, 1]]
    xs = [boxf[0, 0], boxf[1, 0], boxf[2, 0], boxf[3, 0]]
    ys_sorted_index = np.argsort(ys)
    xs_sorted_index = np.argsort(xs)
    x1 = boxf[xs_sorted_index[0], 0]
    x2 = boxf[xs_sorted_index[3], 0]
    y1 = boxf[ys_sorted_index[0], 1]
    y2 = boxf[ys_sorted_index[3], 1]
    img_org2 = img.copy()
    img_plate = img_org2[y1:y2, x1:x2]
    #cv2.imshow('number plate', img_plate)
    #cv2.imwrite("./plate.png",img_plate)
    #cv2.waitKey(0)
    #cv2.destroyAllWindows()
    return img_plate

if __name__ == '__main__':
    imagePath = './car1.png'
    img = cv2.imread(imagePath)
    cv2.imshow('img', img)
    plate=detect(img)